SwisTrack - A Flexible Open Source Tracking Software for Multi-Agent Systems

Lochmatter, Thomas; Roduit, Pierre; Cianci, Christopher M.; Correll, Nikolaus; Jacot, Jacques; Martinoli, Alcherio

doi:10.1109/iros.2008.4650937

Cited by 134 publications

(103 citation statements)

References 10 publications

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“…For all sets, K u = 1/2 and K ω = 2 and the underlying graph is assumed to be fully connected. The position of each robot is monitored with SwisTrack [15] during 60 seconds. After 10 runs, the root mean square error (RMSE) defined by the Euclidean distance between each pair of robot is computed and displayed in Figure 6(a).…”

Section: A Real Robotsmentioning

confidence: 99%

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Bayesian rendezvous for distributed robotic systems

Gowal

Martinoli

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we state, using thorough mathematical analysis, sufficient conditions to perform a rendezvous maneuver with a group of differential-wheeled robots endowed with an on-board, noisy, local positioning system. In particular, we extend the existing framework of noise-free, graph-based distributed control with a layer of Bayesian reasoning allowing to solve the rendezvous problem more efficiently in presence of uncertainties and in a probabilistically sound way. Finally we perform extensive experiments with a team of four real robots, and simulation with their corresponding simulated counterpart, to confirm the benefits of our Bayesian approach.

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Section: A Real Robotsmentioning

confidence: 99%

“…Second we use a range noise σ e = 0. 15[m] and a bearing noise σ α = 1 [rad]. After 1000 runs, the RMSE is computed and displayed in Figure 6(c).…”

Section: B Simulationmentioning

confidence: 99%

Bayesian rendezvous for distributed robotic systems

Gowal

Martinoli

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…A twocolor passive marker located at the top of each block is used in conjunction with SwisTrack [15], an open-source software package developed in our laboratory, to track the blocks. Both their position (x,y) and orientation (θ) are logged in real time at a rate of approximately 10 Hz.…”

Section: Methodsmentioning

confidence: 99%

A trajectory-based calibration method for stochastic motion models

Mario

Mermoud

Mastrangeli

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we present a quantitative, trajectory-based method for calibrating stochastic motion models of water-floating robots. Our calibration method is based on the Correlated Random Walk (CRW) model, and consists in minimizing the Kolmogorov-Smirnov (KS) distance between the step length and step angle distributions of real and simulated trajectories generated by the robots. First, we validate this method by calibrating a physics-based motion model of a single 3-cm-sized robot floating at a water/air interface under fluidic agitation. Second, we extend the focus of our work to multi-robot systems by performing a sensitivity analysis of our stochastic motion model in the context of SelfAssembly (SA). In particular, we compare in simulation the effect of perturbing the calibrated parameters on the predicted distributions of self-assembled structures. More generally, we show that the SA of water-floating robots is very sensitive to even small variations of the underlying physical parameters, thus requiring real-time tracking of its dynamics.

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“…An overhead camera was used with SwisTrack [9] to capture the ground truth at 20 Hz, while the robot odometry was polled at 100 Hz. The camera was calibrated using Tsai's method, reporting a mean distance error of 2 mm over the 16 calibration points.…”

Section: Odometrymentioning

confidence: 99%

The Khepera IV Mobile Robot: Performance Evaluation, Sensory Data and Software Toolbox

Soares

Navarro

Martinoli

2015

Advances in Intelligent Systems and Computing

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Abstract. Taking distributed robotic system research from simulation to the real world often requires the use of small robots that can be deployed and managed in large numbers. This has led to the development of a multitude of these devices, deployed in the thousands by researchers worldwide. This paper looks at the Khepera IV mobile robot, the latest iteration of the Khepera series. This full-featured differential wheeled robot provides a broad set of sensors in a small, extensible body, making it easy to test new algorithms in compact indoor arenas. We describe the robot and conduct an independent performance evaluation, providing results for all sensors. We also introduce the Khepera IV Toolbox, an open source framework meant to ease application development. In doing so, we hope to help potential users assess the suitability of the Khepera IV for their envisioned applications and reduce the overhead in getting started using the robot.

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SwisTrack - A Flexible Open Source Tracking Software for Multi-Agent Systems

Cited by 134 publications

References 10 publications

Bayesian rendezvous for distributed robotic systems

Bayesian rendezvous for distributed robotic systems

A trajectory-based calibration method for stochastic motion models

The Khepera IV Mobile Robot: Performance Evaluation, Sensory Data and Software Toolbox

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